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Guy R. Davies

Publications and source records attributed to Guy R. Davies.

At least 19 recordsLinked to original sources

Ensemble asteroseismology: An ensemble approach to detecting signatures of solar-like oscillations in K-dwarfs

Solar-like oscillations have to date been observed in hundreds of main-sequence and sub-giant stars. However, only a handful of detections have been made in K-type dwarfs, using ground-based extreme precision radial velocity observations and space-based photometric observations made by the NASA \emph{Kepler} and TESS missions. Whilst the upcoming ESA PLATO Mission promises to add to these individual detections, it will do so only in a similar, modest number of stars. Here, we propose a new ensemble strategy to exploit the PLATO data, in which frequency power spectra on hundreds of K-dwarfs lying in constrained ranges of effective temperature are combined in a weighted manner to significantly improve the detectability of the oscillations. Whilst this approach means it is not possible to extract usable constraints on individual oscillation frequencies, it provides a way to detect and measure the characteristics of the composite envelope of oscillation power given by the ensemble, which in turn provides diagnostics of granulation and magneto-convection and the impact of magnetic activity on the modes. We use data in the PLATO Input Catalogue (PIC) to make discrete numerical predictions of the detectability of the ensemble spectra. We also derive a simple analytical approximation of our method that obviates the need to perform numerical calculations over a discrete sample of targets, and which serves as a useful tool to make quick predictions for other future or planned missions. Our predictions indicate that PLATO has the potential to provide solid ensemble detections well into the K-dwarf regime. In summary, PLATO offers an ideal opportunity to exploit this new approach.

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gr8stars II : judgement day for spectroscopic parameter model systematics

Many areas of astrophysics, including exoplanetary studies, rely on precise and accurate stellar parameters. This demands that uncertainties on these parameters truly reflect all biases and systematics. Within this second work of the \texttt{gr8stars} collaboration, we take a set of 585 bright FGK dwarfs with high resolution, high signal-to-noise ratio spectra from the SOPHIE spectrograph. We determine stellar effective temperature, surface gravity, and metallicity using five different spectroscopic methods for each star, with an additional method used for comparisons. We find a typical scatter of 76 K in \teff, 0.14 dex in \logg, and 0.07 dex in \feh. These deviations are significantly larger than the average precision error on these parameters. We furthermore use isochrone fitting to determine mass, radius, and age for all 585 stars, using input from all results. We use the radii determined by SED fitting in the first \texttt{gr8stars} paper as a comparison to our isochronal radii from this work, in addition to comparing the isochronal \logg to spectroscopic \logg. The scatter in mass and radius from the use of different spectroscopic methods is investigated and propagated to exoplanetary parameters. The induced fractional uncertainties in planetary radius ($\lesssim$ 3 \%) and mass ($\lesssim$ 5\%) are found to be below those typically found in the literature. We estimate a lower limit on planetary equilibrium temperature fractional uncertainty of $\approx$ 4\%, a noise floor that is currently not sufficiently represented in the literature.

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Granulation signatures as seen by Kepler short-cadence data. II. A hierarchical route to inferring stellar radii from granulation

Stellar granulation arises from near-surface convection and is imprinted in stellar photometric time series, yet links between granulation observables and fundamental stellar properties remain underexploited. We aim to establish a statistically robust framework for inferring stellar radii directly from granulation signals in long-duration space-based photometry, aided by atmospheric parameters. We construct a Bayesian hierarchical model to connect stellar radius and granulation, relating radius through regression to the total granulation amplitude, primary characteristic frequency of the granulation, stellar effective temperature, and surface metallicity. The derivation is performed separately for three granulation models, propagating the marginal posteriors of the granulation parameters to account for intrinsic dispersion of the derived relations. Each background model yields a unique radius posterior, subsequently combined using Bayesian evidences as weights, producing posteriors that best represent the given star. The granulation-radius relations were derived from a heterogeneous sample of 363 stars, combining seismic and interferometric targets from multiple sources. Application to an independent sample of 367 stars recovers the reference radii within $1σ$ in ${\approx}73\%$ of cases. The distribution of residuals is consistent with a well-calibrated and unbiased inference. Across applications, the granulation-inferred radii achieve a precision of ${\approx}10\%$. The agreement with seismic and interferometric benchmarks demonstrates that granulation carries predictive information on stellar radii at a level comparable to several established techniques. Using granulation as a structural diagnostic enables the inference of stellar radii from granulation signals across diverse stellar populations; directly applicable to data from Kepler, TESS, and the upcoming ESA PLATO mission.

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Assessment of PLATO Science Performance

The PLATO mission is scheduled for launch early 2027. In this paper we present an overview of the performance drivers for the mission at the time where all flight models of the cameras have been tested and integrated on the optical bench. The PLATO consortium needs an estimate of the planet detection yield to dimension the ground-based radial velocity follow-up resources. We provide updated estimates on the yield of planet detections that can be expected from the mission under certain assumptions. As of today, large uncertainties remain on the planet occurrence rates, especially for small planets in long-period orbits, and on our ability to detect these planets in the presence of stellar variability and instrumental noise. To partially overcome these limitations, we compare results using different planet occurrence rates, detectability rates, and we include an estimate on the expected contribution of stellar variability to the noise budget. The final detection yield of PLATO will provide constraints to planet occurrence rates which in turn will help constraining planet formation models.

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Granulation signatures as seen by Kepler short-cadence data. I. A decoupling between granulation and oscillation timescales for dwarfs

Granulation is the observable signature of convection in envelopes of low-mass stars, forming the background in stellar power spectra. While well-studied in evolved giants, granulation on the MS has received less attention. We here study and characterise granulation signatures of MS and SGB stars, extending previous studies of giants to provide a continuous physical picture across evolutionary stages. We analyse 753 Kepler short-cadence stars using a Bayesian nested-sampling framework to evaluate three background descriptions and compare model preferences. This yields full posterior distributions for all parameters, enabling robust comparisons across a diverse stellar sample. No universal preference between background models is found. Assuming a Gaussian oscillation envelope, $ν_\mathrm{max}$ estimates are sensitive to model misspecification, with the resulting systematics exceeding the formal uncertainties. The envelope width scales with $ν_\mathrm{max}$ across models and shows a dependence on effective temperature. Total granulation amplitudes in dwarfs broadly follow giant-based scalings, however a decoupling appears between the timescale of the primary granulation and the oscillations for MS stars cooler than the Sun. The prolonged granulation timescale is reproduced by 3D simulations of a K-dwarf, driven by reduced convective velocities due to more efficient convective energy transport in denser envelopes. The prolonged granulation timescale increases the frequency separation to the oscillation excess, potentially aiding seismic detectability, while the reduced convective velocities may influence the excitation of stellar oscillations and relate to the low amplitudes observed in cool dwarfs. Finally, we contribute a dataset linking granulation, oscillations, and stellar parameters, providing a foundation for future investigations into their interdependence across the HR diagram.

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Asteroseismology of solar-like oscillators: emulating individual mode frequencies with a branching neural network

Accurately measuring stellar ages and internal structures is challenging, but the inclusion of asteroseismic observables can substantially improve precision. However, the curse of dimensionality means this comes at a high computational cost when using standard interpolation methods across grids of stellar models. Furthermore, without a rigorous treatment of random uncertainties in grid-based modelling, it is not possible to address systematic errors in stellar models. We present PITCHFORK -- a multilayer perceptron neural network with a branching architecture capable of rapid emulation of both classical stellar observables and individual asteroseismic oscillation modes of solar-like oscillators. PITCHFORK can predict the classical observables $T_{\text{eff}}$, $L$, and $\left[\mathrm{Fe}/\mathrm{H}\right]$ with precisions of $5.88\,\text{K}$, $0.014\,\text{L}_{\odot}$, and $0.001\,\text{dex}$, respectively, and can predict 35 individual radial mode frequencies with a uniform precision of $0.02$ per cent. PITCHFORK is coupled to a vectorised Bayesian inference pipeline to return well-sampled and fully marginalised posterior distributions. We validate our rigorous treatment of the random uncertainties -- including the asteroseismic surface effect -- in an extensive hare-and-hounds exercise. We also demonstrate our ability to infer the stellar properties of benchmark stars -- namely, the Sun and the binary stars 16 Cygni A and B. This work demonstrates a computationally scalable and statistically robust framework for stellar parameter inference of solar-like oscillators using individual asteroseismic mode frequencies. This provides a foundation for the treatment of systematics in preparation for the imminent abundance of asteroseismic data from future missions.

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Peakbagging the K2 KEYSTONE sample with PBjam: characterising the individual mode frequencies in solar-like oscillators

The pattern of individual mode frequencies in solar-like oscillators provides valuable insight into their properties and interior structures. The identification and characterisation of these modes requires high signal-to-noise and frequency resolution. The KEYSTONE project unlocks the asteroseismic potential of the K2 mission by providing individually reduced, high-quality time series data, global asteroseismic parameters, and spectroscopic analysis for 173 solar-like oscillators. In this work, we build on the KEYSTONE project and present the first analysis of the pattern of individual modes in the oscillation spectra for the K2 KEYSTONE stars. We perform a robust identification and characterisation of the modes through peakbagging methods in the open-source analysis tool PBjam. We present over 6000 mode frequencies, widths, and heights for 168 stars in the sample, covering the HR diagram from FGK dwarfs to sub-giants and the lower red giant branch, providing a significant increase in the number of individual mode frequency detections for main sequence and sub-giant oscillators. This study also presents sample-wide trends of oscillation patterns as a function of the fundamental stellar properties, and improves the precision of the global asteroseismic parameters. These measurements are part of the legacy of the K2 mission, and can be used to perform detailed modelling to improve the precision of fundamental properties of these stars. The results of this analysis provides evidence for the validity of using PBjam to identify and characterise the modes resulting from the observations of the future PLATO mission.

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Granulation signatures in 3D hydrodynamical simulations: evaluating background model performance using a Bayesian nested sampling framework

Understanding the granulation background signal is of vital importance when interpreting the asteroseismic diagnostics of solar-like oscillators. Various descriptions exist in the literature for modelling the surface manifestation of convection, the choice of which affects our interpretations. We aim to evaluate the performance of and preference for various granulation background models for a suite of 3D hydrodynamical simulations of convection across the HR diagram, thereby expanding the number of simulations and coverage of parameter space for which such studies have been made. We take a statistical approach by considering the granulation in power density spectra of 3D simulations, where no biases or systematics of observational origin are present. To properly contrast the performance of the models, we develop a Bayesian nested sampling framework for model inference and comparison. This framework was extended to real stellar data using KIC 8006161 (Doris) and the Sun. We find that multi-component models are consistently preferred over a single-component model, with each tested multi-component model demonstrating merit in specific cases. This occurs for simulations with no magnetic activity, thus ruling out stellar faculae as the sole source of the second granulation component. Like a previous study, we find that a hybrid model with a single overall amplitude and two characteristic frequencies performs well for numerous simulations. Additionally, a tentative third granulation component beyond the value of $ν_\mathrm{max}$ is seen for some simulations, but its potential presence in observations requires further efforts. Studying the granulation signatures in these simulations paves the way to studying stars with accurate granulation models. This deeper understanding of the granulation signal may lead to complementary methods to existing algorithms for determining stellar parameters.

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gr8stars I: A homogeneous spectroscopic study of bright FGKM dwarfs and a public library of their high-resolution spectra

As the fields of stellar and exoplanetary study grow and revolutionary new detection instruments are created, it is imperative that a homogeneous, precise source of stellar parameters is available. This first work of the gr8stars collaboration presents the all-sky magnitude limited sample of 5645 bright FGKM dwarfs, along with homogeneously derived spectroscopic parameters of a subset of 1716 targets visible from the Northern hemisphere. We have collected high-resolution archival and new spectra from several instruments. Spectrosocpic parameters are determined using the PAWS pipeline, employing both the curve-of-growth equivalent width method, and the spectral synthesis method. We achieve median uncertainties of 106K in stellar effective temperature, 0.08 dex in surface gravity, and 0.03 dex in metallicity. This paper also presents photometric stellar parameters for these dwarfs, determined using SED fitting. The full gr8stars sample selection, including derived spectroscopic and photometric parameters, is made available through an interactive online database. We also perform a kinematic analysis to classify these stars according to their Galactic component.

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Asteroseismic Signatures of Core Magnetism and Rotation in Hundreds of Low-Luminosity Red Giants

Red Giant stars host solar-like oscillations which have mixed character, being sensitive to conditions both in the outer convection zone and deep within the interior. The properties of these modes are sensitive to both core rotation and magnetic fields. While asteroseismic studies of the former have been done on a large scale, studies of the latter are currently limited to tens of stars. We aim to produce the first large catalogue of both magnetic and rotational perturbations. We jointly constrain these parameters by devising an automated method for fitting the power spectra directly. We successfully apply the method to 302 low-luminosity red giants. We find a clear bimodality in core rotation rate. The primary peak is at $δν_{\mathrm{rot}}$ = 0.32 $μ$Hz, and the secondary at $δν_{\mathrm{rot}}$ = 0.47 $μ$Hz. Combining our results with literature values, we find that the percentage of stars rotating much more rapidly than the population average increases with evolutionary state. We measure magnetic splittings of 2$σ$ significance in 23 stars. While the most extreme magnetic splitting values appear in stars with masses > 1.1M$_{\odot}$, implying they formerly hosted a convective core, a small but statistically significant magnetic splitting is measured at lower masses. Asymmetry between the frequencies of a rotationally split multiplet has previously been used to diagnose the presence of a magnetic perturbation. We find that of the stars with a significant detection of magnetic perturbation, 43\% do not show strong asymmetry. We find no strong evidence of correlation between the rotation and magnetic parameters.

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Reggae: A Parametric Tuner for PBJam, and a Visualization Tool for Red Giant Oscillation Spectra

The upcoming second release of PBJam -- a software instrument for fitting normal modes ("peakbagging") -- supplements the simple power-spectrum model used in the first version to additionally constrain other features. Dipole ($\ell = 1$) modes, which had been excluded in the initial version of the tool, are now specifically included. The primary samples of the PLATO mission consist mainly of main-sequence and subgiant stars, so PBjam implements a single parameterisation of dipole mixed-mode frequencies that reduces to pure p-modes in the former, and is suitable for use with the latter, outside the red-giant "asymptotic" regime. In keeping with the overall philosophy of PBjam's design, PBjam 2 will specify prior distributions on these parameters empirically, through predetermined values found for existing samples of solar-like oscillators. While the red-giant asymptotic regime has been extensively characterised observationally, the nonasymptotic construction for subgiants here has not, requiring us to construct this prior sample ourselves. To assist in this task, we built a tool -- Reggae -- to manually fine-tune and fit the dipole-mode model, and check the quality of both our initial guesses and fitted solutions. We have found it very helpful both for these tuning and visualisation tasks, and also as a didactic aid to understanding the dipole mixed-mode parameters. Moreover, no other tools currently exist for performing these tasks in the nonasymptotic parameterisation considered here. As such, we release Reggae publicly in advance of this update to PBjam, as we believe the community will benefit from access to such a visualisation tool. This will also assist future users of PBjam in devising ad-hoc prior constraints on the mixed-mode parameters, should they wish to perform mode identification for anomalous stars.

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The K2 Asteroseismic KEYSTONE sample of Dwarf and Subgiant Solar-Like Oscillators. I: Data and Asteroseismic parameters

The KEYSTONE project aims to enhance our understanding of solar-like oscillators by delivering a catalogue of global asteroseismic parameters (${Δν}$ and ${ν_{\rm max}}$) for 173 stars, comprising mainly dwarfs and subgiants, observed by the K2 mission in its short-cadence mode during campaigns 6-19. We derive atmospheric parameters and luminosities using spectroscopic data from TRES, astrometric data from $\textit{Gaia}$, and the infrared flux method (IRFM) for a comprehensive stellar characterisation. Asteroseismic parameters are robustly extracted using three independent methods, complemented by an iterative refinement of the spectroscopic analyses using seismic ${\log g}$ values to enhance parameter accuracy. Our analysis identifies new detections of solar-like oscillations in 159 stars, providing an important complement to already published results from previous campaigns. The catalogue provides homogeneously derived atmospheric parameters and luminosities for the majority of the sample. Comparison between spectroscopic ${T_{\rm eff}}$ and those obtained from the IRFM demonstrates excellent agreement. The iterative approach to spectroscopic analysis significantly enhances the accuracy of the stellar properties derived.

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Asteroseismology of $δ$ Scuti stars: emulating model grids using a neural network

Young $δ$ Scuti stars have proven to be valuable asteroseismic targets but obtaining robust uncertainties on their inferred properties is challenging. We aim to quantify the random uncertainties in grid-based modelling of $δ$ Sct stars. We apply Bayesian inference using nested sampling and a neural network emulator of stellar models, testing our method on both simulated and real stars. Based on results from simulated stars we demonstrate that our method can recover plausible posterior probability density estimates while accounting for both the random uncertainty from the observations and neural network emulation. We find that the posterior distributions of the fundamental parameters can be significantly non-Gaussian, multi-modal, and have strong covariance. We conclude that our method reliably estimates the random uncertainty in the modelling of $δ$ Sct stars and paves the way for the investigation and quantification of the systematic uncertainty.

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Stellar Cruise Control: Weakened Magnetic Braking Leads to Sustained Rapid Rotation of Old Stars

Despite a growing sample of precisely measured stellar rotation periods and ages, the strength of magnetic braking and the degree of departure from standard (Skumanich-like) spindown have remained persistent questions, particularly for stars more evolved than the Sun. Rotation periods can be measured for stars older than the Sun by leveraging asteroseismology, enabling models to be tested against a larger sample of old field stars. Because asteroseismic measurements of rotation do not depend on starspot modulation, they avoid potential biases introduced by the need for a stellar dynamo to drive starspot production. Using a neural network trained on a grid of stellar evolution models and a hierarchical model-fitting approach, we constrain the onset of weakened magnetic braking. We find that a sample of stars with asteroseismically-measured rotation periods and ages is consistent with models that depart from standard spindown prior to reaching the evolutionary stage of the Sun. We test our approach using neural networks trained on model grids produced by separate stellar evolution codes with differing physical assumptions and find that the choices of grid physics can influence the inferred properties of the braking law. We identify the normalized critical Rossby number ${\rm Ro}_{\rm crit}/{\rm Ro}_\odot = 0.91\pm0.03$ as the threshold for the departure from standard rotational evolution. This suggests that weakened magnetic braking poses challenges to gyrochronology for roughly half of the main sequence lifetime of sun-like stars.

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Systematics in Asteroseismic Modelling: Application of a Correlated Noise Model for Oscillation Frequencies

The detailed modelling of stellar oscillations is a powerful approach to characterising stars. However, poor treatment of systematics in theoretical models leads to misinterpretations of stars. Here we propose a more principled statistical treatment for the systematics to be applied to fitting individual mode frequencies with a typical stellar model grid. We introduce a correlated noise model based on a Gaussian Process (GP) kernel to describe the systematics given that mode frequency systematics are expected to be highly correlated. We show that tuning the GP kernel can reproduce general features of frequency variations for changing model input physics and fundamental parameters. Fits with the correlated noise model better recover stellar parameters than traditional methods which either ignore the systematics or treat them as uncorrelated noise.

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A Catalogue of Solar-Like Oscillators Observed by TESS in 120-second and 20-second Cadence

The Transiting Exoplanet Survey Satellite (TESS) mission has provided photometric light curves for stars across nearly the entire sky. This allows for the application of asteroseismology to a pool of potential solar-like oscillators that is unprecedented in size. We aim to produce a catalogue of solar-like oscillators observed by TESS in the 120-second and 20-second cadence modes. The catalogue is intended to highlight stars oscillating at frequencies above the TESS 30-minute cadence Nyquist frequency with the purpose of encompassing the main sequence and subgiant evolutionary phases. We aim to provide estimates for the global asteroseismic parameters $ν_{\mathrm{max}}$ and $Δν$. We apply a new probabilistic detection algorithm to the 120-second and 20-second light curves of over 250,000 stars. This algorithm flags targets that show characteristic signatures of solar-like oscillations. We manually vet the resulting list of targets to confirm the presence of solar-like oscillations. Using the probability densities computed by the algorithm, we measure the global asteroseismic parameters $ν_{\mathrm{max}}$ and $Δν$. We produce a catalogue of 4,177 solar-like oscillators, reporting $Δν$ and $ν_{\mathrm{max}}$ for $98\%$ of the total star count. The asteroseismic data reveals vast coverage of the HR diagram, populating the red giant branch, the subgiant regime and extending toward the main sequence. A crossmatch with external catalogs shows that 25 of the detected solar-like oscillators are a component of a spectroscopic binary, and 28 are confirmed planet host stars. These results provide the potential for precise, independent asteroseismic constraints on these and any additional TESS targets of interest.

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The K2 Galactic Archaeology Program Data Release 3: Age-abundance patterns in C1-C8 and C10-C18

We present the third and final data release of the K2 Galactic Archaeology Program (K2 GAP) for Campaigns C1-C8 and C10-C18. We provide asteroseismic radius and mass coefficients, $κ_R$ and $κ_M$, for $\sim 19,000$ red giant stars, which translate directly to radius and mass given a temperature. As such, K2 GAP DR3 represents the largest asteroseismic sample in the literature to date. K2 GAP DR3 stellar parameters are calibrated to be on an absolute parallactic scale based on Gaia DR2, with red giant branch and red clump evolutionary state classifications provided via a machine-learning approach. Combining these stellar parameters with GALAH DR3 spectroscopy, we determine asteroseismic ages with precisions of $\sim 20-30\%$ and compare age-abundance relations to Galactic chemical evolution models among both low- and high-$α$ populations for $α$, light, iron-peak, and neutron-capture elements. We confirm recent indications in the literature of both increased Ba production at late Galactic times, as well as significant contribution to r-process enrichment from prompt sources associated with, e.g., core-collapse supernovae. With an eye toward other Galactic archaeology applications, we characterize K2 GAP DR3 uncertainties and completeness using injection tests, suggesting K2 GAP DR3 is largely unbiased in mass/age and with uncertainties of $2.9\%\,(\rm{stat.})\,\pm0.1\%\,(\rm{syst.})$ & $6.7\%\,(\rm{stat.})\,\pm0.3\%\,(\rm{syst.})$ in $κ_R$ & $κ_M$ for red giant branch stars and $4.7\%\,(\rm{stat.})\,\pm0.3\%\,(\rm{syst.})$ & $11\%\,(\rm{stat.})\,\pm0.9\%\,(\rm{syst.})$ for red clump stars. We also identify percent-level asteroseismic systematics, which are likely related to the time baseline of the underlying data, and which therefore should be considered in TESS asteroseismic analysis.

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Modelling stars with Gaussian Process Regression: Augmenting Stellar Model Grid

Grid-based modelling is widely used for estimating stellar parameters. However, stellar model grid is sparse because of the computational cost. This paper demonstrates an application of a machine-learning algorithm using the Gaussian Process (GP) Regression that turns a sparse model grid onto a continuous function. We train GP models to map five fundamental inputs (mass, equivalent evolutionary phase, initial metallicity, initial helium fraction, and the mixing-length parameter) to observable outputs (effective temperature, surface gravity, radius, surface metallicity, and stellar age). We test the GP predictions for the five outputs using off-grid stellar models and find no obvious systematic offsets, indicating good accuracy in predictions.As a further validation, we apply these GP models to characterise 1,000 fake stars. Inferred masses and ages determined with GP models well recover true values within one standard deviation. An important consequence of using GP-based interpolation is that stellar ages are more precise than those estimated with the original sparse grid because of the full sampling of fundamental inputs.

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